AI Trainer (Earth & Environmental Sciences) - Remote

Job Description: Computational Life Scientist – AI Training & Model Evaluation

Role Type: ContractorLocation: Global (Fully Remote)Time Commitment: 15+ hours per weekSchedule: Flexible

About the Role

We are seeking experienced Computational Life Scientists with prior professional AI-training or model-evaluation experience to contribute to a project focused on improving advanced AI systems.

You will apply your scientific expertise and programming skills to develop challenging technical tasks, evaluate AI-generated code and analyses, identify errors, and establish objective standards for accurate solutions. This opportunity is intended for professionals with substantive experience in AI training involving life sciences or biomedical content.

Key Responsibilities

1. Create realistic computational life-science tasks that challenge advanced AI models.
2. Develop clear task instructions, reproducible environments, reference solutions, and evaluation criteria.
3. Evaluate AI-generated code, analyses, models, experiments, and technical deliverables.
4. Identify invalid biological assumptions, methodological errors, data leakage, statistical issues, and reproducibility failures.
5. Assess the scientific accuracy, reliability, and completeness of model-generated outputs.
6. Establish objective standards for evaluating computational and scientific solutions.

Required Qualifications

1. Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
2. Strong practical proficiency in Python.
3. Deep expertise in at least one relevant life-science specialization.
4. Hands-on experience using computational methods for modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.

Relevant Specializations

• Biology
• Computational Biology
• Bioinformatics
• Medicine and Health
• Biomedical Science
• Neuroscience
• Genomics
• Ecology and Evolution

Public Proof of Technical Expertise

Strong applicants should be able to demonstrate their expertise through evidence such as:

• Peer-reviewed publications or preprints involving computational methods.
• Public research code or bioinformatics pipelines.
• Google Scholar, ORCID, or equivalent research profiles.
• PhysioNet or other biomedical competition results.
• Open-source scientific software contributions.
• Public datasets, models, notebooks, or technical projects.

Preferred Qualifications

1. Experience creating AI-training tasks, reference answers, evaluation rubrics, graders, or benchmark problems.
2. Experience reviewing technical submissions from other AI trainers.
3. Familiarity with NumPy, SciPy, pandas, Biopython, PyTorch, JAX, or comparable scientific tools.
4. Previous experience with long-horizon or agentic AI evaluations.
5. Availability to begin immediately.

Application Process

1. Application Submission: Submit your application along with details of your AI-training experience, life-science specialization, and public technical evidence.
2. AI Interview: Complete an AI interview that includes technical and integrity checks.
3. Hiring Manager Review: The team evaluates your profile and interview performance.
4. Onboarding: Selected candidates complete project onboarding and begin their first assigned task.

Compensation

Compensation is output-based, with payment for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.

Originally posted on Himalayas